News

Our institute supports, as partner, the first edition of the Transylvanian Machine Learning Summer School (TMLSS), which will take place in Cluj, Transylvania, Romania in July 16-22, 2018. The focus of school will be on Deep Learning and Reinforcement Learning. The school is organized by machine learning researchers from DeepMind (Răzvan Pașcanu, Viorica Pătrăucean), Romanian Institute of Science and Technology (Luigi Malagò, Răzvan Florian), and McGill University & DeepMind (Doina Precup).

James Watson has been awarded the 1962 Nobel Prize in Physiology or Medicine for his contribution to the discovery of the molecular structure of nucleic acids and its significance for information transfer in living material.

Vasile V. Moca, Raul C. Mureșan and colleagues have recently shown that membrane resonance, a property of inhibitory neurons, may hold the key to the emergence of robust and stable gamma oscillations in the brain.

RIST's scientist Marius F. Danca and his colleagues have discovered that the Mandelbrot set is not only the set of complex plane points for which Julia sets are connected, but also the set of all parameter values for which alternated Julia sets are disconnected.

RIST's Marius F. Danca has found that if the control parameter p, of a continuous-time nonlinear system belonging to a large class of systems, is switched within a set of chosen values in a deterministic or even random manner, while the underlying model is numerically integrated, the obtained attractor is a numerical approximation of one of the existing attractors of the considered system.

Our scientists have found that the internal timescale of the brain, i.e., the time window needed by neurons to encode a given aspect of the visual stimulus, is tightly correlated to the external timescale of the visual stimulus, i.e., the speed with which the visual image on the retina changes.

The institute's scientists have developed a special visualization technique for how multiple neurons fire spikes together to encode information, by representing the identity of firing patterns of multiple neurons with color sequences.

2% for RIST

Joining our team

We welcome new scientists who would like to join our institute as principal investigators. Read more.

Automated software development through abstraction in deep, distributed computational models

Institutul Român de Știință și Tehnologie is the beneficiary of this project, the purpose of which is the development of disruptive methods for automatizing software development and for simplifying the work of programmers, by applying state of the art machine learning technologies and cognitive theories.

Specific objectives / foreseen results:- Methods for automated creation of of new implementations of software;- Tools to help programmers debug and maintain complex software and to facilitate software development.

The project is co-funded by the European Regional Development Fund through the Competitiveness Operational Programme 2014-2020. Total value of the project: 8.687.180 lei. Eligible irredeemable value from the European Regional Development Fund: 7.274.674,88 lei. Eligible irredeemable value from national budget: 1.340.525,12 lei.

Riemannian optimization methods for deep learning

Institutul Român de Știință și Tehnologie is the beneficiary of this project, the purpose of which is the design and analysis of novel training algorithms for neural networks in deep learning, by applying notions of Riemannian optimization and differential geometry.

Specific objectives / foreseen results:- The definition of a probabilistic and geometric framework for the study of deep neural networks aimed at a better understanding of the working mechanisms behind the success of deep learning;- A complete characterization of the second-order Riemannian and affine geometries of a statistical model, aimed at the study of second-order optimization methods over statistical manifolds;- The design and implementation of new first and second-order methods for the optimization of functions defined over a statistical model, and in particular in the case of the training of neural networks;- An empirical evaluation of the algorithms proposed for the training of deep neural networks over standard datasets and in industrial competitions, aimed at showing the effectiveness of our methods with respect to the state of the art, in the fields of image and video, text document and audio document analysis;- A feasibility study, comprising a market analysis and the identification of novel potential markets for successful applications of deep learning;- The implementation of specific innovative demo applications based on deep learning technologies, and in particular on the algorithms developed during the project.

The project is co-funded by the European Regional Development Fund through the Competitiveness Operational Programme 2014-2020. Total value of the project: 8.689.500 lei. Eligible irredeemable value from the European Regional Development Fund: 7.276.617 lei. Eligible irredeemable value from national budget: 1.340.883 lei.